NTL Converging Constraints

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NTL Converging Constraints

• Basic concepts and words derive their meaning
  from embodied experience.
• Abstract and theoretical concepts derive their
  meaning from metaphorical maps to more basic
  embodied concepts.
• Structured Connectionist Models can capture both
  of these processes nicely.
• Grammar extends this by Constructions pairings
  of form with embodied meaning.

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Simulation-based language understanding
Utterance
Harry walked to the cafe.
Constructions
Analysis Process
General Knowledge
Simulation Specification
Schema Trajector Goal walk Harry cafe
Belief State
Simulation
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Background Primate Motor Control

• Relevant requirements (Stromberg, Latash, Kandel,
  Arbib, Jeannerod, Rizzolatti)
• Should model coordinated, distributed,
  parameterized control programs required for motor
  action and perception.
• Should be an active structure.
• Should be able to model concurrent actions and
  interrupts.
• Model
• The NTL project has developed a computational
  model based on that satisfies these requirements
  (x- schemas).
• Details, papers, etc. can be obtained on the web
  at http//www.icsi.berkeley.edu/NTL

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Active representations

• Representation based on stochastic Petri nets
  captures dynamic, parameterized nature of actions
• Many inferences about actions derive from what we
  know about executing them
• Generative model action, planning, recognition,
  language.

Walking bound to a specific walker with a
direction or goal consumes resources (e.g.,
energy) may have termination condition(e.g.,
walker at goal) ongoing, iterative action
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Somatotopy of Action Observation
Foot Action
Hand Action
Mouth Action
Buccino et al. Eur J Neurosci 2001
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Active Motion Model
Evolving Responses of Competing Models over Time.
Nigel Goddard 1989
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Language Development in Children

• 0-3 mo prefers sounds in native language
• 3-6 mo imitation of vowel sounds only
• 6-8 mo babbling in consonant-vowel segments
• 8-10 mo word comprehension, starts to lose
  sensitivity to consonants outside native language
• 12-13 mo word production (naming)
• 16-20 mo word combinations, relational words
  (verbs, adj.)
• 24-36 mo grammaticization, inflectional
  morphology
• 3 years adulthood vocab. growth,
  sentence-level grammar for discourse purposes

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food toys misc. people
sound emotion action prep.
demon. social
Words learned by most 2-year olds in a play
school (Bloom 1993)
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Learning Spatial Relation Words Terry Regier

• A model of children learning spatial relations.
• Assumes child hears one word label of scene.
• Program learns well enough to label novel scenes
  correctly.
• Extended to simple motion scenarios, like INTO.
• System works across languages.
• Mechanisms are neurally plausible.

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Learning System
dynamic relations (e.g. into)
structured connectionistnetwork (based on
visual system)

• Well look at the details next lecture

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Limitations

• Scale
• Uniqueness/Plausibility
• Grammar
• Abstract Concepts
• Inference
• Representation
• Biological Realism

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Constrained Best Fit in Nature
inanimate animate
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Learning Verb MeaningsDavid Bailey

• A model of children learning their first verbs.
• Assumes parent labels childs actions.
• Child knows parameters of action, associates with
  word
• Program learns well enough to
• 1) Label novel actions correctly
• 2) Obey commands using new words (simulation)
• System works across languages
• Mechanisms are neurally plausible.

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Motor Control (X-schema) for SLIDE
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Parameters for the SLIDE X-schema
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Feature Structures for PUSH
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System Overview
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Learning Two Senses of PUSH
Model merging based on Bayesian MDL
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Training ResultsDavid Bailey

• English
• 165 Training Examples, 18 verbs
• Learns optimal number of word senses (21)
• 32 Test examples 78 recognition, 81 action
• All mistakes were close lift yank, etc.
• Learned some particle CXN,e.g., pull up
• Farsi
• With identical settings, learned senses not in
  English

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Constrained Best Fit in Nature
inanimate animate
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Compositionality

• Traditional
• Context-free composition of logical forms
• Contemporary
• Constructional composition of conceptual
  
• frames
• Formal Cognitive Linguistics

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Embodied Construction Grammar(Bergen, Chang
Paskin 2000)

• Assumptions from Construction Grammar
• Constructions are form-meaning pairs(Lakoff
  1987, Goldberg 1995)
• Constructions vary in degree of specificity and
  level of description (morphological, lexical,
  phrasal, clausal)
• Constructions evoke and bind semantic schemas
• Additional influences
• Cognitive Grammar (Langacker 1987)
• Frame Semantics (Fillmore 1982)
• Structured Connectionism (Feldman 1987)

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Traditional Levels of Analysis
Pragmatics
Semantics
Syntax
Morphology
Phonetics
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Harry walked into the cafe.
Pragmatics
Semantics
Utterance
Syntax
Morphology
Phonetics
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Language understanding analysis simulation
construction WALKED in context c constituents
form f of type wakt meaning walking construed
as Walk-Action semantic constraints walking.ti
me before c.speech-time walking.aspect
encapsulated designates walking
Harry walked into the cafe.
Utterance
Analysis Process
Constructions
General Knowledge
Simulation Specification
Belief State
Simulation
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Simulation specification

• A simulation specification consists of
• semantic schemas evoked by constructions
• bindings between schemas (labeled by the
  constructions that enforce them)

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Conclusion

• Language acquisition and use is a hallmark of
  being human
• Language seems to rely on fine-grained aspects of
  embodied (sensory-motor and social cognition)
  primitives and brain-like computation (massively
  parallel, distributed, spreading activation,
  temporal binding).
• Understanding requires imaginative simulation!
• We have built a pilot system that demonstrates
  the use of motor control representations in
  grounding the language of abstract actions and
  policies.
• Sensory-Motor imagination and simulation is
  crucial in interpretation!
• Ongoing Work.
• Formalize and use a compositional set of embodied
  conceptual primitives and grammatical
  constructions.
• Perform both behavioral and fMRI imaging
  experiments to test the predictions of the
  simulation hypothesis.
• Further refine and ground the model in details of
  neural anatomy and functional architecture
  (basal-thalamic-cortical loops).